End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNN

TL;DR

This paper presents a deep learning framework utilizing 3D-CNNs for accurate end-to-end diagnosis of Parkinson's disease from brain MRI scans and personal data, aiming to improve diagnostic confidence.

Contribution

It introduces a novel deep learning approach combining classification and regression for Parkinson's diagnosis using MRI and demographic data.

Findings

High accuracy in Parkinson's classification

Effective integration of MRI and personal information

Potential to assist clinical diagnosis

Abstract

In this work, we use a deep learning framework for simultaneous classification and regression of Parkinson disease diagnosis based on MR-Images and personal information (i.e. age, gender). We intend to facilitate and increase the confidence in Parkinson disease diagnosis through our deep learning framework.